Wayne F. Velicer

The transtheoretical model of health behavior change

The transtheoretical model posits that health behavior change involves progress through six stages of change: precontemplation, contemplation, preparation, action, maintenance, and termination. Ten processes of change have been identified for producing progress along with decisional balance, self-efficacy, and temptations. Basic research has generated a rule of thumb for at-risk populations: 40% in precontemplation, 40% in contemplation, and 20% in preparation. Across 12 health behaviors, consistent patterns have been found between the pros and cons of changing and the stages of change. Applied research has demonstrated dramatic improvements in recruitment, retention, and progress using stage-matched interventions and proactive recruitment procedures. The most promising outcomes to date have been found with computer-based individualized and interactive interventions. The most promising enhancement to the computer-based programs are personalized counselors. One of the most striking results to date for stage-matched programs is the similarity between participants reactively recruited who reached us for help and those proactively recruited who we reached out to help. If results with stage-matched interventions continue to be replicated, health promotion programs will be able to produce unprecedented impacts on entire at-risk populations.

The Process of Smoking Cessation: An Analysis of Precontemplation, Contemplation, and Preparation Stages of Change.

Traditionally smoking cessation studies use smoker and nonsmoker categories almost exclusively to represent individuals quitting smoking. This study tested the transtheoretical model of change that posits a series of stages through which smokers move as they successfully change the smoking habit. Subjects in precontemplation (n = 166), contemplation (n = 794), and preparation (n = 506) stages of change were compared on smoking history, 10 processes of change, pretest self-efficacy, and decisional balance, as well as 1-month and 6-month cessation activity. Results strongly support the stages of change model. All groups were similar on smoking history but differed dramatically on current cessation activity. Stage differences predicted attempts to quit smoking and cessation success at 1- and 6-month follow-up. Implications for recruitment, intervention, and research are discussed.

Determining the number of components from the matrix of partial correlations

A common problem for both principal component analysis and image component analysis is determining how many components to retain. A number of solutions have been proposed, none of which is totally satisfactory. An alternative solution which employs a matrix of partial correlations is considered. No components are extracted after the average squared partial correlation reaches a minimum. This approach gives an exact stopping point, has a direct operational interpretation, and can be applied to any type of component analysis. The method is most appropriate when component analysis is employed as an alternative to, or a first-stage solution for, factor analysis.

Decisional balance measure for assessing and predicting smoking status.

The Decisional Balance Sheet of Incentives has been proposed by Janis and Mann (1977) as a general schema for representing both the cognitive and motivational aspects of human decision making. In this study, a brief 24-item paper and pencil measure was constructed to study the decision-making process in smoking cessation. Two scales were identified and labeled the Pros of Smoking and the Cons of Smoking. These scales were successful in differentiating between five groups representing stages of change in the quitting process. The two scales were also successful when employed as predictors of smoking status at a 6-month follow-up. The Decisional Balance Scale appears to be a powerful construct of potentially wide application in behavior change.

Assessing outcome in smoking cessation studies.

Outcome measures for smoking cessation are reviewed and evaluated, including 3 self-report measures and 3 biochemical validation measures. Point prevalence reflects the percentage of participants taking action, prolonged abstinence reflects those in the maintenance stage, and continuous abstinence reflects those who progress from action to maintenance without lapsing or relapsing. Biochemical assessments are primarily measures of point prevalence abstinence. The desirability of biochemical validation is a particularly controversial and critical issue. Three factors affect the accuracy of self-report: Type of Population, Type of Intervention, and Demand Characteristics. False-negative rates are generally low. Three broad issues impact on decisions to use biochemical validation: (a) alternative explanations for false positives, (b) refusal rate problems, and (c) the effect of inaccuracy on intervention assessment.

Standardized, individualized, interactive, and personalized self-help programs for smoking cessation.

Smokers (N = 756) were randomly assigned by stage of change to (a) standardized self-help manuals (ALA+ condition), (b) individualized manuals matched to stage (TTT condition), (c) interactive expert-system computer reports plus individualized manuals (ITT condition), or (d) a personalized condition with 4 counselor calls, stage manuals, and computer reports (PITT condition). Over 18 months, the ITT groups results more than doubled those of the ALA+ group on abstinence measures. The ALA+ and TTT conditions were equivalent over 12 months, but at 18 months the TTT condition was more effective. The ITT condition was the best or comparable with the best treatment at all follow-ups for smokers at all stages of change. Results suggest that an effective expert system has been developed, and discussion focuses on delivering this system to entire populations of smokers.

Relapse situations and self-efficacy: An integrative model☆☆☆

Researchers studying relapse for an addictive behavior have employed two different conceptual models. Researchers concerned with typologies of relapse situations have developed a variety of discrete classes of high risk situations. Researchers who have employed a Self-efficacy approach have typically assessed different situations but scored the measure as a single general construct. Using structural modeling, this paper evaluates five alternative measurement models, representing alternative conceptualizations. A hierarchical model which integrates the previously competing models provided the best fit to the data and serves to explain a large body of previous findings. The model includes three first order constructs (Positive/Social; Negative/Affective; and Habit/Addictive) and one general second-order factor. The results were replicated across two different response formats and two different subject samples.

Construct Explication through Factor or Component Analysis: A Review and Evaluation of Alternative Procedures for Determining the Number of Factors or Components

The concept of a construct is central to many of the advances in the behavioral sciences during the second half of this century. Constructs serve to summarize, organize, and facilitate the interpretation of data. The concept of a construct also permits us to move directly from data analysis to theory development and testing. Factor analysis and component analysis are two very similar methods that facilitate the transition from dealing with a large number of observed variables to a smaller number of constructed or latent variables. Douglas Jackson employed factor or component analysis as an integral part of his sequential approach to the development of psychological measures (Jackson, 1970, 1971). It has become a standard part of measure development and is one of the most employed statistical procedures in the behavioral sciences.

Component Analysis versus Common Factor Analysis: Some issues in Selecting an Appropriate Procedure

Should one do a component analysis or a factor analysis? The choice is not obvious, because the two broad classes of procedures serve a similar purpose, and share many important mathematical characteristics. Despite many textbooks describing common factor analysis as the preferred procedure, principal component analysis has been the most widely applied. Here we summarize relevant information for the prospective factor/component analyst. First, we discuss the key algebraic similarities and differences. Next, we analyze a number of theoretical and practical issues. The more practical aspects include: the degree of numeric similarity between solutions from the two methods, some common rules for the number of factors to be retained, effects resulting from overextraction, problems with improper solutions, and comparisons in computational efficiency. Finally, we review some broader theoretical issues: the factor indeterminacy issue, the differences between exploratory and confirmatory procedures, and the issue of latent versus manifest variables.

The Transtheoretical Model of Change and HIV Prevention: A Review

The transtheoretical model of health behavior change is described and supporting empirical work is presented that reviews the central constructs of the model: the stages of change, processes of change, decisional balance, confidence, and temptation. Model-based applications to a broad range of problem behaviors are summarized. Applications to human immunodeficiency virus (HIV) prevention behavior changes are highlighted for each variable. Finally, several questions about the area of sexual behavior change to reduce risk of HIV exposure are explored and future research ideas are described within the context of this model.